Bibliographic record
Abstract
In this survey, we present many results about quasi-cluster algebras. In the first section, we present a comprehensive study of non-oriented surfaces, focusing on their triangulations and arc flips. Drawing from Wilson's work \[Discrete Comput. Geom. 59 (2018), 680–706], we analyze properties of the cluster complex. Next, we provide the definition of quasi-cluster algebras, originally introduced by Dupont and Palesi, and highlight some of their properties, notably their connection to the cluster categories. Furthermore, we present Wilson's formula \[arXiv:1912.12789v1] for directly computing cluster variables, bypassing the need for recursion. Finally, we introduce Wilson's alternative definition \[Int. Math. Res. Not. IMRN (2018), 3800–3833, and Selecta Math. (N.S.) 26 (2020), article no. 72], which endows these algebras with Laurent phenomenon algebra properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".